Open Access
| Issue |
BIO Web Conf.
Volume 194, 2025
International Scientific Conference on Biotechnology and Food Technology (BFT-2025)
|
|
|---|---|---|
| Article Number | 01096 | |
| Number of page(s) | 10 | |
| DOI | https://doi.org/10.1051/bioconf/202519401096 | |
| Published online | 14 November 2025 | |
- S. Kujawa, G. Niedbała, Artificial Neural Networks in Agriculture. Agriculture 11(6), 497 (2021). https://doi.org/10.3390/agriculture11060497 [Google Scholar]
- Y. Wang, Deep Learning Methods Used in Precision Agriculture. BIO Web of Conferences 142, 01004 (2024). https://doi.org/10.1051/bioconf/202414201004 [Google Scholar]
- M. Albahar, A Survey on Deep Learning and Its Impact on Agriculture. Agriculture 13, 540 (2023). https://doi.org/10.3390/agriculture13030540 [Google Scholar]
- Y. Wang et al., Progress in Research on Deep Learning‑Based Crop Yield Prediction. Agronomy 14, 2264 (2024). https://doi.org/10.3390/agronomy14102264 [Google Scholar]
- J. Logeshwaran et al., Improving Crop Production Using an Agro‑Deep Learning Framework in Precision Agriculture. BMC Bioinformatics 25, 341 (2024). https://doi.org/10.1186/s12859-024-05970-9 [Google Scholar]
- A.Oikonomidis, et.al., Deep learning for crop yield prediction: a systematic literature review. New Zealand Journal of Crop and Horticultural Science, 51(1), 1–26. (2022). https://doi.org/10.1080/01140671.2022.2032213 [Google Scholar]
- S. Coulibaly, et.al., Deep Learning for Precision Agriculture: A Bibliometric Analysis. Intelligent Systems with Applications 16, 200102 (2022). https://doi.org/10.1016/j.iswa.2022.200102 [CrossRef] [Google Scholar]
- M. Nafityal, et.al., Revolutionizing agriculture: A comprehensive review on artificial intelligence applications in enhancing properties of agricultural produce. Food Chemistry: X 29, 102748 (2025). https://doi.org/10.1016/j.fochx.2025.102748 [Google Scholar]
- M. Jabed, et.al., Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability. Heliyon, 10, 24, e40836 (2024). https://pmc.ncbi.nlm.nih.gov/articles/PMC11667600 [Google Scholar]
- X-B Jin, et.al., Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System. Sustainability 12(4), 1433 (2020). https://doi.org/10.3390/su12041433 [CrossRef] [Google Scholar]
- W. Maes, K. Steppe, Perspectives for Remote Sensing with UAV in Precision Agriculture. Trends Plant Sci. 24, 152–164 (2019). https://doi.org/10.1016/j.tplants.2018.11.007 [CrossRef] [Google Scholar]
- Z. Chu, J. Yu, An End‑to‑End Model for Rice Yield Prediction Using Deep Learning Fusion. Comput. Electron. Agric. 174, 105471 (2020). https://doi.org/10.1016/j.compag.2020.105471 [CrossRef] [Google Scholar]
- Anna X. Wang, Caelin Tran, Nikhil Desai, David Lobell, and Stefano Ermon. 2018. Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data. In Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS ‘18). Association for Computing Machinery, New York, NY, USA, Article 50, 1–5. https://doi.org/10.1145/3209811.3212707 [Google Scholar]
- Md. Ashav Noman Mahin, Md. Nasim Adnan, Rahamatullah Khondoker et al. Precision Agriculture using Machine Learning and Deep Learning Algorithms: A Comprehensive Study, 05 May 2025, PREPRINT (Version 1) available at Research Square. https://doi.org/10.21203/rs.3.rs-6589718/v1 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.